Course Director: Pragya Kakani, PhD
This course has two aims. First, to introduce students to fundamental health economic topics The course is designed is accomplish these two aims using a three-pronged approached.
(1) Lectures: The first half of the course will introduce a health economic topic that will provide context for the class (2) Course readings: The second half of the course will focus on one or two class readings that both relate to the (3) Course assignments: There will be a small number of course assignments that will require students to use Stata.
Course Director: Yongkang Zhang, PhD
This course offers a comprehensive introduction to applied machine learning models in the context of health policy and health economics. Students will learn how to develop, evaluate, and deploy machine learning models to study important health policy and economic topics across different care settings, such as predicting mortality to support hospice decision making, risk adjustment for insurance plans, and machine learning boosted causal inference methods for health policy evaluations. Throughout the course, we will focus on understanding the potential disparities associated with machine learning-based decision making in health policy and health economics.
Course Director: Ali Jalali, PhD
This course provides students with an introduction to health economic modeling and statistical methods central to applied pharmacoeconomics. Through lectures and applied exercises, students will develop a working knowledge of how to select, justify, and apply appropriate statistical distributions to represent a range of health outcomes and cost measures in pharmacoeconomic evaluations of medical products and healthcare programs. Students will also learn how to identify and synthesize multiple sources of evidence in developing computational models for applied pharmacoeconomic analyses. Through hands-on exercises in Microsoft Excel, students will gain practical experience applying parametric, non-parametric, and simulation-based methods using common healthcare data sources.
Course Director: Chang Su, PhD
Introduces students to a variety of analytic methods for health data using computational tools. The course covers topics in data mining, machine learning, classification, clustering and prediction. Students engage in hands-on exercises using a popular collection of data mining algorithms.
Course Director: Fei Wang, PhD
3 credits
Prerequisite: Artificial Intelligence in Medicine I
This class will teach students more advanced topics on AI in medicine. It requires students to have taken the AI in medicine I class. The contents of the class cover generalizability of AI models, computational fairness, model interpretation and explanation, privacy and security, federated learning, multi-modal learning, generative AI, causal inference, target trial emulation. The students will be asked to do a final project with teams based on the contents taught in the class, and python programming will be needed for doing the project.
Course Director: Samprit Banerjee, PhD, MStat
There has been an explosion of big data in medicine and healthcare. There are four main sources of such big data – 1) administrative databases in healthcare such as electronic health records and health insurance claims, 2) biomedical imaging (e.g. MRI, CT-Scan, X-ray etc.) 3) sensors in smartphones, wearable and implantable devices and 4) genetics and genomics. It is difficult to navigate and critically assess the statistical methods and analytic tools that are needed to conduct analytics and research with such big biomedical data. This course will introduce the four above-mentioned important sources of big data in medical studies, discuss the nuances and intricacies of how such data are generated and introduce tools to navigate such databases visualize and describe them.
Course Director: Xi Kathy Zhou, PhD
This course introduces the fundamentals of biostatistics with a primary emphasis on the understanding of statistical concepts behind data analytic principles through applications in biomedical studies. This course will enhance students’ proficiency in using R, a freely available software, to explore, visualize, and perform statistical analysis with data. Topics covered include: exploratory data analysis; basic concepts of statistics; construction of hypothesis tests and confidence intervals; performance of statistical comparisons; simple modeling; and determination of power and sample size.
Course Director: Yushu Shi, PhD
This course aims to introduce some common statistical methods and computational tools for predictive modeling, specifically regression analysis. Topics covered in this course include:
- Multivariable linear regression, variable selection, and model diagnosis
- Linear regression with variable transformation
- Generalized linear models, including Logistic regression model, Poisson regression model, variations of the Poisson model for zero-inflated data, multinomial Logistic regression model, and relevant model diagnoses
- Survival analysis: censoring mechanism, log-rank test, Kaplan-Meier curve, parametric survival models, including Cox model (and Fine and Gray model for competing risks data.)
Materials in parentheses are subject to students’ background, performance of past exams, and class progress.
Course Director: Linda Gerber, PhD
3 credits
The course will deliver an advanced topic in data analysis that is extremely important for an independent biostatistician: Hierarchical (Mixed Effect) Models with Applications in Longitudinal Data Analysis. This course will give students necessary tools to analyze clustered data, data with hierarchical structure, previously analyzed data from multiple sources (meta-analysis), repeated measure and longitudinal data. The course will cover how to model and analyze such data, and how to remedy missing values in such data. The course will also incorporate instruction in the statistical software R. Every statistical technique will be subsequently followed by instruction and demonstration in R, analyzing real data.
Course Directors: Marianne Sharko, MD
Prerequisites: Introduction to Health Informatics
Clinical information systems such as electronic health records are central to modern healthcare. This course introduces students to the complex infrastructure of clinical information systems, technologies used to improve healthcare quality and safety (including clinical decision support and electronic ordering), and policies surrounding health information technology.
Course Director: Faculty
3 credits
In addition to technical, programming and analytical skills, healthcare informaticians and data scientists need clinical domain expertise to understand and interpret real world data and analytical findings and to communicate effectively with healthcare practitioners and investigators. This course is designed to equip informaticians with a foundational understanding of key concepts in clinical medicine, especially as they relate to the collection, application and interpretation of real world data toward clinical phenotypes and predictive analytics. Students will learn the fundamentals of the cardiovascular, gastrointestinal, respiratory, hematological, endocrine, neurological, musculoskeletal, psychiatric, and renal systems and how diseases in these body systems are reflected in subjective and objective measures collected through patient reports, clinical observations, laboratory tests and ancillary studies. Students will understand the clinicians approach to ordering tests to evaluate for the presence of disease. They will also learn about the variety and classification of pharmacological therapies, the context and rationale for starting and stopping medications, and their intended and unintended effects on body systems. Students will also learn how the physical and social environment in which patients live may impact the recognition and severity of illness, as well as the timing, approach and outcomes of care. Students will be introduced to differentiated care in the management of different patient specialties, including pediatrics and geriatrics.
Course Director: Ali Jalali, PhD
Prerequisites: Biostatistics I or Introduction to Biostatistics
The cost effectiveness analysis course is a 2 part course. The first part provides an overview of techniques used to understand medical decision making under uncertainty. Participants will learn how to structure decision analysis questions, construct decision trees, and analyze outcomes using probability. The second part provides an in-depth exposure to techniques used to conduct economic evaluations of health care technologies and programs. Participants learn how to critique economic evaluations using cost-effectiveness approaches and are introduced to tools they can use to apply these techniques in their own research projects.
Course Director: Mila Sun, PhD, MS
This is a practical introductory course in R programming and unsupervised learning. Students will learn to create clear and effective visualizations, manipulate and summarize complex datasets, and develop interactive tools for data exploration. This course covers foundational topics in unsupervised learning, including dimension reduction, clustering methods, and matrix factorization with illustrative applications. The primary goal of this course is to equip students with the skills and conceptual understanding needed to explore data structure, identify patterns, and critically apply unsupervised learning methods in practice.
Course Director: Samprit Banerjee, PhD, MStat
In the last decade, biomedical and health sciences have seen an explosion of “Big Data” problems. Such problems are commonly associated with general business analytics and marketing. Many statistical and machine learning methods are required to solve such problems. This course is going to provide the basic know-how to tackle such problems and is going to teach what is statistical learning, how to assess model accuracy, supervised and unsupervised classification techniques, tree-based methods, random forests, regularized regression techniques, resampling methods, and support vector machines. The aim of this course is to enable students to identify an appropriate statistical learning algorithm for a real-world application and be able to apply the algorithm to the data using R while being cognizant of the advantages and disadvantages of the chosen algorithm.
Course Director: Shoshana Rosenberg, ScD, MPH
Students have the option of taking PHSC 9001 in Fall Term 1 OR Fall Term 2. Those who want to take PHSC 9002 in Spring must take PHSC 9001 in Fall Term I.
The goal of this course is to provide students with a foundation of epidemiologic methods. This course will introduce students to key epidemiologic concepts including measures of disease frequency, study designs, bias, and causal inference. Students will also learn how to critically evaluate epidemiological research papers.
Course Director: Kevin Kensler, ScD
3 credits
Prerequisite: Epidemiology I (PHSC 9001)
The goal of the course is to provide students with more advanced epidemiologic methods and statistical analyses appropriate for specific study designs. This course will expand students’ knowledge of epidemiologic concepts related to the design, conduct and interpretation of epidemiologic studies.
Course Director: Wodan Ling, PhD
Must receive the instructor’s approval and/or pass a screening test.
This course provides an introduction to the fundamentals of Python programming with an emphasis on core techniques and tools used in AI and data science. Students will learn essential programming concepts, including data structures, control flow, functions, and classes. The course will cover data processing using libraries such as NumPy and Pandas, data visualization using Matplotlib and Seaborn, and basic statistical analysis with SciPy. Foundational AI-related methods such as the Monte Carlo method (including random number generation, simulation, and numerical integration) and numerical optimization will be introduced, with applications to real-world problems in AI and data science.
Course Director: Faculty
Consumer health informatics (CHI) is the study of consumer information needs and technologies that provide consumers with the information they need to be more engaged in self-care and healthcare. This introductory CHI course will present an overview of theories of health and information behavior; key concepts and terminology; and main application domains. We will explore how health behavior theories 8 provide a framework for explaining consumers’ health behaviors and how CHI tools that are built with a theoretical foundation can promote health behavior change. The course will cover CHI applications in major application domains including electronic patient portals, mobile health (mHealth), and telehealth. Students will learn how to assess end-user needs and technological practices of potential users who experience health information and technological disparities. Students will also learn how to design for endusers, evaluate CHI applications and research.
Course Director: Angelica Meinhofer, PhD
This course provides an introduction to data sources commonly used for health research. Topics will include identifying an appropriate data source for a project, assessing the strengths and weaknesses of data sources, managing and analyzing datasets, and SAS programming.
Course Director: Yiye Zhang, PhD
Database systems are central to most organizations’ information systems strategies. At any organizational level, users can expect to have frequent contact with database systems. Therefore, skill in using such systems – understanding their capabilities and limitations, knowing how to access data directly or through technical specialists, knowing how to effectively use the information such systems can provide, and skills in designing new systems and related applications – is a distinct advantage and necessity today. The Relational Database Management System (RDBMS) is one type of database systems that are most often used in healthcare organizations and is the primary focus of this course. An overview of the non-relational database structure will also be given using Python programming language to provide a fuller picture of the current data management landscape. Further, to provide students with opportunities to apply the knowledge they learn from the lectures, various homework assignments, lab assignments, an exam, and a database implementation project will be given.
Course Director: Amelia Bond, PhD, MS, MHS
Health Economics is an elective course that introduces students to core economic principles underlying health, health care, insurance markets, and health policy. Through lectures, readings, data analysis exercises in Stata, and applied assignments, students will examine topics such as adverse selection, moral hazard, provider markets, health disparities, pharmaceutical innovation, and the drivers of health care spending. The course emphasizes applying economic concepts to contemporary policy and business issues, equipping students with tools to evaluate health care systems and ongoing health policy debates.
Course Director: Faculty
In a modern healthcare system, exchange of clinical data across multiple stakeholders – between healthcare organizations, between providers and patients, and among agencies and governmental entities – is pivotal. Health information standards provide the “backbone” to achieve uniform data interoperability and exchange across multiple heterogeneous systems. This course will introduce various existing and emerging clinical data modeling, terminology and knowledge representation standards that are part of Meaningful Use Regulations, and discuss scenarios and use cases where the standards have been applied for routine clinical practice and research.
Course Director: Sam Solomon
The US healthcare system is in the midst of transformational changes that have been catalyzed in part by the continued effects of the Affordable Care Act and the 2008 recession. This course will look at the major trends occurring in healthcare from a provider viewpoint, how leaders are both responding to and anticipating these changes, and how these changes will shape the healthcare system of the future. The goal of this course is to provide students with an understanding of the nature and context of the changes happening in healthcare, while also offering real-world perspectives from industry leaders who will speak to how they are adapting to and even shaping these changes in their roles. Upon completing this course, both clinical and non-clinical students will have gained greater insight into the healthcare system, which they will be able to apply to their current and future roles.
Course Director: Lisa Kern, MD, MPH
The goal of this course is to educate students about the complexity and nuances of healthcare delivery. The course will be especially useful for non-clinicians who intend to go into fields that will require a detailed understanding of healthcare. Class sessions will not summarize healthcare; rather, they will analyze healthcare – through themes such as people, time, money, communication, uncertainty, and others. Students will come away from the course with a deeper appreciation of why it is difficult to change healthcare. They will then be able to anticipate the intended and unintended consequences of interventions and policies that they and others might implement.
Course Director: Faculty
3 credits
This course provides an overview of implementation science and examines ethical, methodological, and practical challenges in the use of artificial intelligence (AI) in healthcare. It focuses on the safe and effective development, evaluation, and implementation of predictive models, large language models, and generative AI systems in real-world clinical settings.
The course introduces frameworks for evaluating AI systems using the principles of fairness, appropriateness, validity, effectiveness, and safety (FAVES), along with performance metrics, generalizability, and robustness across populations and healthcare environments. It examines sources of bias, distribution shift, and unintended consequences, and explores ethical issues through the lens of autonomy, beneficence, nonmaleficence, and justice. Students will analyze trade-offs between performance, fairness, privacy, and safety, and consider the implications for different stakeholders, including patients, providers, and health systems.
The course also covers key concepts in data governance, privacy, and human factors, as well as implementation science frameworks such as Diffusion of Innovation, RE-AIM, and PRISM. Emphasis is placed on translating AI models into practice, including workflow integration, monitoring, and real-world deployment challenges. In addition, the course introduces emerging topics such as large language models and agentic AI systems for clinical decision support and healthcare operations.
We will invite experts in the field to provide guest lectures and to lead student workshops within their areas of expertise.
Course Director: Yuhua Bao, PhD
Economic incentives embedded in the health care system shape the behaviors of key stakeholders. This course provides an overview and analysis of incentives in the current US health care system for consumers/patients, health care providers, payers and insurers, and other stakeholders such as pharmaceutical and medical device companies. Discussion centers around how the medical care market differs from markets for other goods and services and how incentives interact to affect health care delivery and outcomes. We then use the lens of incentives to examine the rationale and consequences – both intended and unintended – of major reform models designed to align incentives with improving the quality and experience of care while containing the growth of health care costs.
Course Director: Angelica Meinhofer, PhD
Prerequisites: Biostatistics I or Introduction to Biostatistics
With an emphasis on empirical applications, this course equips students with the tools necessary to empirically analyze non-experimental data at levels often required in professional environments. Applied Econometrics for Health Policy is designed with twin objectives in mind. The first is to provide students with the ability to critically analyze the empirical analysis done by others at a level sufficient to make intelligent decisions about how to use that analysis in the design of health policy. The second is to provide students with the skills necessary to perform empirical analysis on their own, or to participate on a team involved in such empirical analysis. Students will become proficient in using multiple regression analysis using cross-sectional and panel data, including in ways that provide causal interpretation.
Course Director: Arindam RoyChoudhury, PhD
An introduction to the fundamentals of biostatistics with primary emphasis on understanding of statistical concepts behind data analytic principles. This course will be accompanied with a Stata lab to explore, visualize and perform statistical analysis with data. Lectures and discussions will focus on the following: exploratory data analysis; basic concepts of statistics; construction of hypothesis tests and confidence intervals; the development of statistical methods for analyzing data; and development of mathematical models used to relate a response variable to explanatory or descriptive variables.
Course Director: Yunyu Xiao, PhD
Health informatics is the body of knowledge that concerns the acquisition, storage, management and use of information in, about and for human health, and the design and management of related information systems to advance the understanding and practice of healthcare, public health, consumer health and biomedical research. The discipline of health informatics sits at the intersection of several fields of research – including health and biomedical science, information and computer science, and sociotechnical and cognitive sciences. In recent years we have witnessed how the collection, storage and usage of digital health data has exponentially grown. Increases in the complexity of health information systems have driven growth in demand for a specialized workforce. This course introduces the field of health informatics and provides students with the basic knowledge and skills to pursue a professional career in this field and apply informatics methods and tools in their health professional practice.
Course Director: Jiani Yu, PhD
This course is designed to introduce students to the fundamentals of health services research. Health services research is the discipline that measures the evaluations of interventions designed to improve healthcare. These interventions can include changes to the organization, delivery and financing of healthcare and various healthcare policies. Common outcome measures in health services research include (but are not limited to) patient safety, healthcare quality, healthcare utilization, and cost. Specific topics to be covered in this course include: refining your research question, identifying common research designs and their strengths and weaknesses, minimizing bias and confounding, selecting data sources, optimizing measurement, and more. There will also be a component of the course that explores how to present your ideas and iteratively refine your work, based on feedback from peers and reviewers. This course includes both lectures and interactive group discussions. Students will be able to apply the methods learned in this course to their masters’ research projects.
Course Director: Nathaniel Hupert, MD, MPH
Every component of health care delivery, from patient scheduling and bed management to information utilization and logistics, is amenable to improvement using approaches based on operations research (OR), the branch of engineering that calls itself “the science of better.” This course will introduce students to key concepts and methods in OR, including queuing theory, simulation, and optimization. Applications using common spreadsheet software and/or free online modeling applications will be emphasized. Student teams will then use these tools to design an efficient, high-performance outpatient clinic.
Course Director: Arian Jung, PhD
This course provides an introduction to basic economic concepts associated with health care and current policy issues facing the US health care system. Topics will include the historical foundations of the health care system, how the health care sector differs from other markets, financing of health care and the role of government, the structure and functions of public and private health insurance, economic components of the delivery system, and understanding the challenges of health care reform. These topics will be examined from the view of payers, providers, and regulators, and the interactions of these stakeholders. Students will also be introduced to international comparisons of health care systems.
Course Director: Yongkang Zhang, PhD
This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.
This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client as well as faculty and fellow classmates.
Course Director: Faculty
This is the culminating capstone course of all masters-level graduate education programs. It has two aims: (1) helping students to discover and develop new and effective ways of managing and working together with all the stakeholders within the healthcare field and (2) helping accelerate a student's development of the context awareness, integrative management, and industry skills that are needed to lead in a rapidly changing healthcare sector. This capstone course puts students in a new organization, one they don’t already know well, and gives them the chance to practice hitting the ground running. This culminating course provides a deeper preparation for the next stages of a student's career. The capstone project will last the entire year: the first term involves matching students with the right project, the second term has students working with their client, and the third term consists of a detailed report and final presentation in front of the client asCou well as faculty and fellow classmates.
Course Director: Yifan Peng, PhD
This course introduces students to the field of natural language processing (NLP), applied to the health domain. NLP focuses on text data, which lacks the structure of conventional tabular data. In the health domain text is abundant in electronic health records, the medical literature and on the Web. Important applications of NLP include information extraction (pulling facts out of text) and information retrieval (searching through a collection of texts). The course presents methods for working with text: identifying the elements (words and symbols), recognizing sentence boundaries, parsing syntactic structures, assigning meaning, and establishing the structure of the discourse as a whole. The students build skills with these methods through laboratory work.
Course Director: Arindam RoyChoudhury, PhD
Pharmaceutical studies use many statistical methods that are not routinely taught as part of conventional biostatistics courses. In this course, the students will learn the statistical methods specifically used in pharmaceutical studies.
The course is divided into three modules.
(1) “Statistical Aspects of Phase I Clinical Trial” will include 3+3 Design, accelerated titration; up and down designs; continual reassessment method (CRM), Modified CRM, TITE CRM, Bayesian Logistic Regression Model (BLRM), escalation with overdose control (EWOC), toxicity probability interval (TPI) and modified TPI (mTPI).
(2) “Statistical Aspects of Phase II Clinical Trial” will include design and analyses for One stage and Simon’s Two Stage Designs, Multi-arm Phase II design.
(3) “Statistical Aspects of Phase III Clinical Trial” will include randomization, design and analysis for parallel, crossover, factorial, seamless Phase II/III, Adaptive and SMART designs.
Course Director: Chengxi Zang, PhD
The course “Python for Health AI” is designed for advanced students and clinicians seeking to develop programming expertise in healthcare applications. This course provides hands-on experience with Python, Pytorch, data science/machine learning libraries, LLM and agents techniques, focusing on real-world health data, including EHRs, medical imaging, and clinical text. Participants will explore AI models for disease prediction, causal inference, and natural language processing etc. The course emphasizes practical implementation, from preprocessing messy health data to deploying AI models in clinical settings. Capstone projects will apply AI to real-world health challenges.
Course Director: Czarina Navos Behrends, PhD, MPH
This course provides an introduction to qualitative theory and methods in health research. Topics will include qualitative research theory, development of qualitative research proposals, interview approaches, qualitative analysis, mixed methods, and theoretical frameworks. The aim of this course is to develop introductory, basic skills for conducting a qualitative research study from beginning to end by providing a combination of education on qualitative theory and providing opportunities to apply that education to a semester long project that mimics a qualitative health research study. This course will use a combination of didactic lectures, discussion, and small group work.
Course Director: Sean Murphy, PhD
Real-world evidence (RWE) is rapidly reshaping how we evaluate the safety, risk, and economic value of medical interventions. Real World Evidence Strategy (HPEC 5015) introduces students to the principles and practices of using real-world data (EMR, claims, registries) to fill critical evidence gaps in the regulatory process. Through a mix of lectures and quantitative labs, students will gain critical competencies in: understanding methodological differences between RWE and randomized clinical trials; RWE regulatory frameworks and advanced methods; and creating professional evidence summaries for regulatory audiences.
Informatics innovations have their desired impact only when they have high quality, are highly usable, are integrated into their organizational setting, and are widely adopted and used. That makes it critical for informatics students to understand not only how informatics innovations work, but also the users and settings in which they are used. Students will learn methods and models for: measuring data and system quality; assessing and predicting technology adoption (what makes technology sticky?); improving human-computer interaction via human factors engineering; understanding organizational and systemic challenges in the real world; influencing patients’ health behavior and decisions; and assessing quality, safety, and cost outcomes using health services research study designs. In this mixed methods course, students will gain experience using both quantitative and qualitative methods.
Course Director: Himel Mallick, PhD
The goal of this course is to introduce a core set of modern statistical and AI-based concepts and techniques to students, and to demonstrate how to use them to answer complex research questions in healthcare. Students will acquire knowledge of causal inference methods that integrate statistical modeling with machine learning and artificial intelligence, including potential outcomes, counterfactuals, directed acyclic graphs, non-parametric structural equation models, inverse probability weighting, g-computation, causal mediation analysis, causal multimodal AI, and precision medicine.
Course Director: Faculty
This one-semester course provides an introduction to statistical computing and modern data analysis using R and Python. Students will learn the complete data analysis workflow, including data import, cleaning, visualization, exploratory data analysis, statistical inference, and model building.
The first part of the course introduces fundamental statistical concepts, including probability, statistical distributions, estimation, confidence intervals, hypothesis testing, and methods for comparing categorical and continuous data, such as chi-square tests, t-tests, analysis of variance (ANOVA), and nonparametric methods. These methods are implemented through hands-on programming exercises in both R and Python.
The second part focuses on statistical modeling and computing, covering simple and multiple linear regression, model diagnostics, variable selection, prediction, and model interpretation. Throughout the course, students will develop practical programming skills for data analysis, visualization, reproducible research, and effective communication of statistical results.
Course Director: Faculty
Course Director: Jialin Mao, MD, PhD
This course will cover the conceptual underpinnings, the policy context, and the methods for comparative effectiveness research (CER) highlighting key issues and controversies. It will provide students with an understanding of the analytic methods and data resources used to conduct comparative effectiveness research. Topics that will be discussed include, observational studies, risk adjustment, propensity score matching, instrumental variables, meta-analysis/systematic reviews and the use of clinical registries and electronic health record data. Students will learn why comparative research has come to prominence, what constitutes good comparative effectiveness research, the main methods used and the advantages and disadvantages of each without being a statistics course. Sessions will consist of lectures from the instructors and experts on selected topics, as well as student discussions and presentations.
Course Director: Sze Yan Liu, PhD, MPH
This course is intended to familiarize students with the theory and application of survey research methods, with an emphasis on application. It will lead students through the process of developing their own survey. Topics will include survey populations and sampling, development of survey instruments, survey administration, post-survey processing and data analysis. Recurring themes throughout these topics are common errors in surveys, their consequences for findings and strategies to minimize these errors in survey design. Students will learn to develop an original research proposal featuring a survey questionnaire as well as critically evaluate existing surveys. The course will be tailored to the specific needs and problems of participants to the extent possible.